Mobility-aware assignment of computational sub-tasks in a vehicular cloud

ABSTRACT

The disclosure includes embodiments for a set of connected vehicles to collectively execute tasks which no single vehicle can execute due to computational limitations of the single vehicle. In some embodiments, a method includes determining, for a vehicular micro cloud, a set of computing sub-tasks to be completed. The method includes determining vehicle travel speeds for the members of the vehicular micro cloud. The method includes assigning the computing sub-tasks to the members based on the vehicle travel speeds of the members relative to one another so that the members that the computational sub-tasks are assigned to the members that are either stationary or traveling at the slowest vehicle travel speeds. The computing sub-task is completed by the member to which it is assigned.

BACKGROUND

The specification relates to mobility-aware assignment of computationalsub-tasks in a vehicular cloud.

Connected vehicles form clusters of interconnected vehicles (e.g., viavehicle-to-everything, i.e., “V2X”) that are located at a similargeographic location. Such clusters are known as “vehicular microclouds.” The vehicles in the cluster make available their unusedcomputing resources to the other members of the vehicular micro cloud.

SUMMARY

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions.

One general aspect includes a computer program product for a vehicularmicro cloud that includes a set of connected vehicles that are operableto collectively execute tasks which no single vehicle can execute due tocomputational limitations of the single vehicle, where the computerprogram product includes a non-transitory memory storingcomputer-executable code that, when executed by a processor, causes theprocessor to: determine, for the vehicular micro cloud, a set ofcomputing sub-tasks to be completed, where the vehicular micro cloudincludes the set of connected vehicles that are members of the vehicularmicro cloud and located in a similar geographic area; determine vehicletravel speeds for the members of the vehicular micro cloud; and assignthe computing sub-tasks to the members based on the vehicle travelspeeds of the members relative to one another so that the members thatthe computational sub-tasks are assigned to the members that are eitherstationary or traveling at the slowest vehicle travel speeds, where thecomputing sub-task is completed by the member to which it is assigned.Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. Thecomputer program product where the computer-executable code iscollaboratively executed by two or more onboard vehicle computers of aplurality of the members of the vehicular micro cloud. The computerprogram product where at least one of the members is a leader vehiclethat solely executes the computer program product and controls whenother members are eligible to leave the vehicular micro cloud. Thecomputer program product where at least one of the members is a leadervehicle that solely executes the computer program product and the leadervehicle is selected based on a set of factors that includes: unusedprocessing power; sensor accuracy; unused bandwidth; and unused memory.The computer program product where the non-transitory memory storesadditional computer-executable code that, when executed by theprocessor, causes the processor to: determine a complexity of thecomputing sub-tasks; determine a sub-task ranking for the computingsub-tasks based on their complexity, where higher sub-task rankings areassigned to the computing sub-tasks that are more complex; and determinea member ranking for the members based on their speeds, where highermember rankings are assigned to the members that are stationary ortraveling at the slowest speeds, where the assigning of the computingsub-tasks to the members is based on the sub-task rankings and themember rankings so that higher ranked sub-tasks are assigned to becompleted by higher ranked members. The computer program product whereeach of the computing sub-tasks is assigned to a member whose memberranking is the same as the sub-task ranking for computing sub-task. Thecomputer program product where a member is assigned a plurality ofsub-tasks. The computer program product where the plurality is executedin an order that is determined based on a set of factors that includeincreasing safety. Implementations of the described techniques mayinclude hardware, a method or process, or computer software on acomputer-accessible medium.

One general aspect includes a method including: determining, for avehicular micro cloud, a set of computing sub-tasks to be completed,where the vehicular micro cloud includes a set of vehicles that aremembers of the vehicular micro cloud and located in a similar geographicarea; determine vehicle travel speeds for the members of the vehicularmicro cloud; and assign the computing sub-tasks to the members based onthe vehicle travel speeds of the members relative to one another so thatthe members that the computational sub-tasks are assigned to the membersthat are either stationary or traveling at the slowest vehicle travelspeeds, where the computing sub-task is completed by the member to whichit is assigned. Other embodiments of this aspect include correspondingcomputer systems, apparatus, and computer programs recorded on one ormore computer storage devices, each configured to perform the actions ofthe methods.

Implementations may include one or more of the following features. Themethod where the steps of the method are executed by an onboard vehiclecomputer of an ego vehicle that is one of the members of the vehicularmicro cloud. The method where at least one of the members is a leadervehicle that executes the method and controls when other members areeligible to leave the vehicular micro cloud. The method where at leastone of the members is a leader vehicle that executes the method and theleader vehicle is selected based on a set of factors that includes:unused processing power; sensor accuracy; unused bandwidth; and unusedmemory. The method further including: determining a complexity of thecomputing sub-tasks; determining a sub-task ranking for the computingsub-tasks based on their complexity, where higher sub-task rankings areassigned to the computing sub-tasks that are more complex; anddetermining a member ranking for the members based on their speeds,where higher member rankings are assigned to the members that arestationary or traveling at the slowest speeds, where the assigning ofthe computing sub-tasks to the members is based on the sub-task rankingsand the member rankings so that higher ranked sub-tasks are assigned tobe completed by higher ranked members. The method where each of thecomputing sub-tasks is assigned to a member whose member ranking is thesame as the sub-task ranking for computing sub-task. The method where amember is assigned a plurality of sub-tasks. The method where theplurality is executed in an order that is determined based on a set offactors that include increasing safety. Implementations of the describedtechniques may include hardware, a method or process, or computersoftware on a computer-accessible medium.

One general aspect includes a system including: an ego vehicle includinga processor executing computer-executable code that is operable, whenexecuted by the processor, to cause the processor to: determine, for avehicular micro cloud, a set of computing sub-tasks to be completed,where the vehicular micro cloud includes a set of connected vehiclesthat are members of the vehicular micro cloud and located in a similargeographic area; determine vehicle travel speeds for the members of thevehicular micro cloud; assign the computing sub-tasks to the membersbased on the vehicle travel speeds of the members relative to oneanother so that the members that the computational sub-tasks areassigned to the members that are either stationary or traveling at theslowest vehicle travel speeds, where the computing sub-task is completedby the member to which it is assigned. Other embodiments of this aspectinclude corresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Thesystem where at least one of the members is a leader vehicle thatexecutes the computer-executable code and controls when other membersare eligible to leave the vehicular micro cloud. The system where atleast one of the members is a leader vehicle that executes thecomputer-executable code and the leader vehicle is dynamically selectedbased on a set of factors that includes: unused processing power; sensoraccuracy; unused bandwidth; and unused memory. Implementations of thedescribed techniques may include hardware, a method or process, orcomputer software on a computer-accessible medium.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is illustrated by way of example, and not by way oflimitation in the figures of the accompanying drawings in which likereference numerals are used to refer to similar elements.

FIG. 1 is a block diagram illustrating an operating environment for anallocation system according to some embodiments.

FIG. 2 is a block diagram illustrating an example computer systemincluding an allocation system according to some embodiments.

FIG. 3 is a flowchart of an example method for allocating sub-tasksaccording to some embodiments.

DETAILED DESCRIPTION

Our invention is motivated by the emerging concept of “vehiclecloudification.” Vehicle cloudification means that vehicles equippedwith on-board computer unit(s) and wireless communicationfunctionalities form a cluster, called a vehicular micro cloud, andcollaborate with other micro cloud members over vehicle-to-vehicle (V2V)networks or V2X networks to perform computation, data storage, and datacommunication tasks in an efficient way. These types of tasks arereferred to herein as “computational tasks.”

Vehicular micro clouds are beneficial, for example, because they helpvehicles to perform computationally expensive tasks that they could notperform alone or store large data sets that they could not store alone.The members that form a vehicular micro cloud may execute computingprocesses (e.g., such as those depicted in FIG. 3) together in parallelby a cooperative process. Individual steps of the computing processesmay be executed by one or more vehicles in a collaborative fashion. Thecooperative process may include the members exchanging V2Xcommunications with one another that communicate outputs of theircomputations or digital data that may be beneficial to other members.

Vehicular micro clouds are described in the patent applications that areincorporated by reference in this paragraph. This patent application isrelated to the following patent applications, the entirety of each ofwhich is incorporated herein by reference: U.S. patent application Ser.No. 15/358,567 filed on Nov. 22, 2016 and entitled “Storage Service forMobile Nodes in a Roadway Area”; U.S. patent application Ser. No.15/799,442 filed on Oct. 31, 2017 and entitled “Service Discovery andProvisioning for a Macro-Vehicular Cloud”; U.S. patent application Ser.No. 15/845,945 filed on Dec. 18, 2017 and entitled “Managed Selection ofa Geographical Location for a Micro-Vehicular Cloud”; and U.S. patentapplication Ser. No. 15/799,963 filed on Oct. 31, 2017 and entitled“Identifying a Geographic Location for a Stationary Micro-VehicularCloud.”

A typical use case of vehicular micro clouds is a data storage service,where vehicles in a micro cloud collaboratively keep data contents intheir on-board data storage device. The vehicular micro cloud allowsvehicles in and around the vehicular micro cloud to request the datacontents from micro cloud member(s) over V2V communications, reducingthe need to access remote cloud servers by vehicle-to-network (e.g.,cellular) communications. For some use cases, micro cloud members mayalso update the cached data contents on the spot with minimalintervention by remote cloud/edge servers (e.g., updating ahigh-definition road map based on measurements from on-board sensors).

The endpoints that are part of the vehicular micro cloud may be referredto herein as “members,” “micro cloud members,” or “member vehicles.”Examples of members include one or more of the following: a connectedvehicle; a roadside device; an edge server; a cloud server; any otherconnected device that has computing resources and has been invited tojoin the vehicular micro cloud by a handshake process. In someembodiments, the term “member vehicle” specifically refers to onlyconnected vehicles that are members of the vehicular micro cloud whereasthe terms “members” or “micro cloud members” is a broader term that mayrefer to one or more of the following: endpoints that are vehicles; andendpoints that are not vehicles such as a roadside device.

As used herein, the term “vehicle” refers to a connected vehicle. Aconnected vehicle is a conveyance, such as an automobile, that includesa communication unit that enables the conveyance to send and receivewireless messages via one or more vehicular networks. Accordingly, asused herein, the terms “vehicle” and “connected vehicle” may be usedinterchangeably. The embodiments described herein are beneficial forboth drivers of human-driven vehicles as well as the autonomous drivingsystems of autonomous vehicles.

A problem is that it can be difficult to determine how to assigncomputational tasks (or computational sub-tasks) to different microcloud members. Described herein are embodiments of an allocation system.An example purpose of the allocation system described herein is toprovide a way to: (1) predict which micro cloud members have the mostcomputing resources available at a future time; and (2) assigncomputational tasks (or computational sub-tasks) to micro-cloud membersbased on these predictions.

Example embodiments of the allocation system are now described. In someembodiments, the allocation system is software that is operable toallocate responsibility for completing computational tasks or sub-tasksbased, in part, on the speed of travel of the micro cloud members of avehicular micro cloud. In some embodiments, the allocation system issoftware installed in an onboard unit (e.g., an electronic control unit(ECU)) of a vehicle having V2X communication capability. The vehicle isa connected vehicle and operates in a roadway environment with N numberof remote vehicles that are also connected vehicles, where N is anypositive whole number.

In some embodiments, the allocation system has two phases as describedbelow. These phases are not a requirement of the allocation system, butmay be included in our preferred embodiment:

Phase 1: Offline Phase

In some embodiments, each vehicle periodically measures its own resourceavailability and its own mobility feature vector. By analyzing thelong-term history of these <resource, mobility feature>pairs, thevehicle can compose a “resource profile” function which allows toestimate available resources based on the mobility feature at a givenpoint in time. This phase is executed by individual connected vehiclesbefore a vehicular micro cloud is formed.

Steps 1-4 of the example general method described below correspond tothe offline phase.

Phase 2: Online Phase

In some embodiments, once a micro cloud is formed, each member sharesits resource profile function with other members. Each member thencollects sensor data to predict its own and other members' mobilityfeature vectors. By putting the predicted mobility feature vectors asinput to the resource profile functions, one or more of micro cloudmembers can predict its own and other members' resource availability atthe future point in time. Finally, micro cloud members assign tasksbased on the predicted resource availability.

Steps 5-14 of the example general method described below correspond tothe online phase.

The following terms are used in this description and are now definedaccording to some embodiments:

Connected Roadside Device: A connected roadside device is a connectedvehicle or a roadside unit having network communication capabilities.The ego vehicle and the remote vehicle are examples of a connectedvehicle.

Traffic Participant: A traffic participant is a connected vehicle thatis in the vicinity of the ego vehicle (e.g., a remote vehicle), apedestrian, a bicyclist, an animal, or any other entity that might bepresent on a roadway occupied by the ego vehicle. The ego vehicle is nota traffic participant.

Example General Method

In some embodiments, the allocation system includes code and routinesthat are operable, when executed by the onboard unit, to cause theonboard unit to execute one or more steps of the following method:

Step 1: Cause the sensor set to collect sensor data. The sensor datadescribes one or more of the following sensor measurements about the egovehicle or a set of traffic participants surrounding the ego vehicle:(i) speed of the ego vehicle; (ii) a speed of the set of trafficparticipants; (iii) geographic position of the ego vehicle; (iv)geographic positions of the set of traffic participants relative to theego vehicle; (v) the number of traffic participants surrounding the egovehicle; (vi) expected stop duration of the ego vehicle; and (vii)expected stop duration of the traffic participants included in the setof traffic participants.

Step 2: Receive V2X data from connected roadside devices (i.e.,connected vehicles and roadside units). The V2X data may include, forexample, BSM data (or CAM data) and/or Signal Phase and Timing (SPaT)data which are routinely received from DSRC-enabled vehicles andconnected roadside devices. The V2X data supplements the sensor data anddescribes similar data as the sensor data with the exception that theV2X data is measured from the perspective of the remote vehicles and/orroadside units and not the ego vehicle.

Step 3: Generate feature data based on the sensor data and/or the V2Xdata. The feature data is digital data that describes a mobility featurevector. An example mobility feature vector includes mi(t) where midescribes the sensor measurements from the sensor data and the V2X dataat different times “t.”

Step 4: Generate profile data for the ego vehicle. The profile data isdigital data that describes a resource profile for the ego vehicle. Anexample resource profile is fi(mi), which is a function of the mobilityfeature vector. In some embodiments, the ego vehicle uses a regressiontechnique to compose the function fi(mi) that models relationshipbetween the mobility feature vector mi and the computing resourcesavailable on the ego vehicle, assuming that the available computingresources have correlation with the mobility feature vector. In someembodiments, each of the other member vehicles include an allocationsystem which execute steps 1-4 above so that each vehicle generatesprofile data describing itself.

Step 5: Form a vehicular micro cloud. This step is background technologydescribed in U.S. patent application Ser. No. 15/799,963, which isincorporated by reference herein. Accordingly, this step is notdescribed in detail. The vehicles that form the vehicular micro cloudare referred to as “members” of the vehicular micro cloud. The membervehicles exchange V2V messages with one another.

Step 6: Each member vehicle sends its own profile data to the othermember vehicles via the V2X network.

Step 7: Determine a computational task to be completed. Thecomputational task may be referred to herein as the “task.” Optionally,transmit a wireless message to the cloud server including digital datadescribing the task to be completed.

Step 8: Cause the sensor set to collect new instances of sensor data.The sensor data describes similar sensor measurements as those describedabove for step 1. Optionally, the traffic participants are the same asthose described by the sensor data described above for step 1.

Step 9: Receive V2X data from the traffic participants. The V2X data mayinclude, for example, BSM (or CAM) data and/or SPaT data. Some of theremote vehicles are those which are included in the set of membervehicles that comprise the vehicular micro cloud. In some embodiments,each micro cloud member transmits V2X data to the ego vehicle so thatthe allocation system can predict resource availability for each ofthese vehicles at step 12.

Step 10: Generate feature data based on the sensor data and the V2Xdata. The feature data is digital data that describes a mobility featurevector. An example mobility feature vector includes mi(t) where midescribes the sensor measurements from the sensor data and the V2X dataat different times “t.”

Step 11: Generate prediction data. The prediction data is digital datathat describes the predicted future behavior of the ego vehicle. This isgenerally a short-term prediction. An example short-term predictionincludes mi(t+Δt).

Step 12: Predict resource availability for the ego vehicle and themember vehicles based on the set of resource profiles generated andreceived at steps 4 and 6, respectively. In some embodiments, this stepis repeated for each of the member vehicles included in the vehicularmicro cloud if sufficient data exist to do so. The output of this stepis the predicted resource availability data. An example of predictedresource availability data for a particular vehicle is described byRi(t)=fi(mi(t+Δt)).

Step 13: Compare the predicted resource availability data of eachvehicle as generated at step 12 to generate the schedule data. Theschedule data describes which member vehicles should execute which tasksor sub-tasks so that the computational task of step 7 is completed.Optionally, this step may be completed by the process manager of thecloud server and the schedule data distributed to the member vehicles bythe process manager. Generally, the tasks will be assigned to thosevehicles which are stopped or traveling at low speeds. Optionally, thesub-tasks may be ranked based on this complexity and the sub-tasks whichare most complex may be assigned to the slow-moving vehicles or stoppedvehicles. Optionally, some of the tasks will be assigned to the cloudserver if micro cloud members are not expected to have sufficientcomputing resources to complete the tasks.

Step 14: The tasks are completed as described by the schedule data andthe output of completing these tasks is shared among the member vehiclesvia V2V communications among the member vehicles.

The steps of the example general method may be executed in any order,and not necessarily those depicted above. In some embodiments, one ormore of the steps are skipped or modified in ways that are describedherein or known or otherwise determinable by those having ordinary skillin the art of vehicular micro clouds.

Leader Vehicle

In some embodiments, the method includes a leader vehicle. For example,the vehicular micro cloud formed by the allocation system includes aleader vehicle that provides the following example functionality:controlling when the set of member vehicles leave the vehicular microcloud; determining how to use the pool of vehicular computing resourcesto complete a set of tasks in an order for the set of member vehicleswherein the order is determined based on a set of factors that includessafety; determining how to use the pool of vehicular computing resourcesto complete a set of tasks that do not include any tasks that benefitthe leader vehicle; and determining when no more tasks need to becompleted, or when no other member vehicles are present except for theleader vehicle, and taking steps to dissolve the vehicular micro cloud.

In some embodiments, the leader vehicle is determined by a set offactors that indicate which vehicle is the most technologicallysophisticated. For example, the member vehicle that has the fastestonboard computer may be the leader vehicle. Other factors that mayqualify a vehicle to be the leader is having the most accurate sensors,most bandwidth, and most memory. Accordingly, the designation of whichvehicle is the leader vehicle may be based on a set of factors thatincludes which vehicle has: (1) the fastest onboard computer; (2) themost accurate sensors; (3) the most bandwidth or other network factorssuch having radios compliant with the most modern network protocols; and(4) most available memory.

In some embodiments, the designation of which vehicle is the leadervehicle changes over time if a more technologically sophisticatedvehicle joins the vehicular micro cloud. Accordingly, the designation ofwhich vehicle is the leader vehicle is dynamic and not static.

In some embodiments, the leader vehicle is whichever member vehicle of avehicular micro cloud has a fastest onboard computer.

Example Benefits

Example benefits of the allocation system relative to the existingsolutions are now described according to some embodiments. In someembodiments, the allocation system includes code and routines that, whenexecuted by a processor, cause the processor to assign tasks to themembers of a vehicular micro cloud based on the speed of the members sothat members that are stationary or traveling at the slowest speeds areassigned tasks whereas those that are traveling relatively faster arenot assigned tasks or only assigned uncomplicated tasks. This isbeneficial because our research indicates that slower moving vehiclesare better at complicated more complicated tasks that vehicles that aretraveling at faster speeds. In some embodiments, the allocation systemincludes code and routines that, when executed by a processor, cause theprocessor to rank the difficulty of tasks, rank the speed of the membersrelative to one another, and then assign the most complex tasks to theslowest traveling members (as well as those that are stationary) and theleast complex tasks to the fastest traveling members. In someembodiments, the allocation system includes code and routines that, whenexecuted by a processor, cause the processor to consider the assignmentof tasks to different onboard vehicle computers based on the speed ofthe vehicle, i.e., the velocity of the vehicle.

The existing solutions do not disclose or suggest assigning tasks to themembers of a vehicular micro cloud based on the speed of the members sothat members that are stationary or traveling at the slowest speeds areassigned tasks whereas those that are traveling relatively faster arenot assigned tasks or only assigned uncomplicated tasks.

The existing solutions also do not disclose or suggest ranking thedifficulty of tasks that need to be completed, ranking the speed of themembers, and then assigning the most complex tasks to the slowesttraveling members (as well as those that are stationary) and the leastcomplex tasks to the fastest traveling members.

With regards to leader vehicles, the existing solutions do not discloseor suggest that a vehicular micro cloud includes a leader vehicle thatprovides the following functionality: (1) controlling when the set ofmember vehicles leave the vehicular micro cloud; (2) determining how touse the pool of vehicular computing resources to complete a set of tasksin an order for the set of member vehicles wherein the order isdetermined based on a set of factors that includes safety; (3)determining how to use the pool of vehicular computing resources tocomplete a set of tasks that do not include any tasks that benefit theleader vehicle; or (4) determining when the queue is no longer presentand taking steps to dissolve the vehicular micro cloud.

The existing solutions also do not disclose or suggest that the leadervehicle is whichever member vehicle that has the fastest onboardcomputer. The existing solutions also do not disclose or suggest thatthe designation of which vehicle is the leader vehicle may be based on aset of factors that includes which member vehicle has: the fastestonboard computer; most accurate sensors; most bandwidth or other networkfactors; and most available memory. The existing solutions also do notdisclose or suggest that the designation of which vehicle is the leadervehicle may change over time if a more technologically sophisticatedvehicle joins the vehicular micro cloud.

Vehicular Micro Clouds

The existing solutions generally do not include vehicular micro clouds.Many groups of vehicles might appear to be a vehicular micro cloud whenthey in fact are not a vehicular micro cloud. For example, in someembodiments a vehicular micro cloud requires that all its members shareit unused computing resources with the other members of the vehicularmicro cloud. Any group of vehicles that does not require all its membersto share their unused computing resources with the other members is nota vehicular micro cloud.

In some embodiments, a vehicular micro cloud does not require a serverand preferably would not include one. Accordingly, in some embodimentsany group of vehicles that includes a sever or whose functionalityincorporates a server is not a vehicular micro cloud.

In some embodiments, a vehicular micro cloud is operable to harness theunused computing resources of many different vehicles to perform complexcomputational tasks that a single vehicle alone cannot perform due tothe computational limitations of a vehicle's onboard vehicle computerwhich are known to be limited. Accordingly, any group of vehicles thatdoes not serve the purpose of harnessing the unused computing resourcesof many different vehicles to perform complex computational tasks that asingle vehicle alone cannot perform is not a vehicular micro cloud.

In some embodiments, vehicles are required to have a predeterminedthreshold of unused computing resources to become members of a vehicularmicro cloud. In some embodiments, a leader of a vehicular micro cloud ispre-designated by a vehicle manufacturer by the inclusion of a bit or atoken in a memory of the vehicle that designates the vehicle as theleader of all vehicular micro clouds which it joins.

A vehicular micro cloud is not a V2X network or a V2V network. Forexample, neither a V2X network nor a V2V network include a cluster ofvehicles in a same geographic region that are computationally joined toone another as members of a logically associated cluster that makeavailable their unused computing resources to the other members of thecluster. In some embodiments, any of the steps of the methods describedherein (e.g., the example general method described above or the methoddepicted in FIG. 3) may be executed by one or more vehicles which areworking together collaboratively using V2X communications for thepurpose of completing one or more steps of the method(s). By comparison,solutions which only include V2X networks or V2V networks do notnecessarily include the ability of two or more vehicles to work togethercollaboratively to complete one or more steps of a method.

Example Operating Environment

The allocation system utilizes a vehicular network in some embodiments.A vehicular network includes, for example, one or more of the following:V2V; V2X; vehicle-to-network-to-vehicle (V2N2V);vehicle-to-infrastructure (V2I); any derivative or combination of thenetworks listed herein; and etc.

In some embodiments, the allocation system includes software installedin an onboard unit of a connected vehicle or an onboard computer of aroadside device such as a Roadside Unit (RSU). This software is the“allocation system” described herein. In some embodiments, theallocation system includes: (1) a mobility predictor; (2) a resourceprofiler; and (3) a task manager. Each of these elements are describedbelow in more detail according to some embodiments.

An example operating environment for the embodiments described hereinincludes an ego vehicle and at least one remote vehicle. The ego vehicleand the remote vehicle are both connected vehicles having communicationunits that enable them to send and receive wireless messages via one ormore vehicular networks. In some embodiments, the ego vehicle and theremote vehicle each include an onboard unit having an allocation systemstored therein.

In some embodiments, the allocation system includes code and routinesthat are operable, when executed by a processor of the onboard unit, tocause the processor to execute one or more of the steps of the examplegeneral method which was described above.

This application is related to U.S. patent application Ser. No.15/644,197 filed on Jul. 7, 2017 and entitled “Computation Service forMobile Nodes in a Roadway Environment,” the entirety of which is herebyincorporated by reference.

A DSRC-equipped device is any processor-based computing device thatincludes a DSRC transmitter and a DSRC receiver. For example, if avehicle includes a DSRC transmitter and a DSRC receiver, then thevehicle may be described as “DSRC-enabled” or “DSRC-equipped.” Othertypes of devices may be DSRC-enabled. For example, one or more of thefollowing devices may be DSRC-equipped: an edge server; a cloud server;a roadside unit (“RSU”); a traffic signal; a traffic light; a vehicle; asmartphone; a smartwatch; a laptop; a tablet computer; a personalcomputer; and a wearable device.

In some embodiments, one or more of the connected vehicles describedabove are DSRC-equipped vehicles. A DSRC-equipped vehicle is a vehiclethat includes a DSRC-compliant GPS unit and a DSRC radio which isoperable to lawfully send and receive DSRC messages in a jurisdictionwhere the DSRC-equipped vehicle is located. A DSRC radio is hardwarethat includes a DSRC receiver and a DSRC transmitter. The DSRC radio isoperable to wirelessly send and receive DSRC messages on a band that isreserved for DSRC messages.

A DSRC message is a wireless message that is specially configured to besent and received by highly mobile devices such as vehicles, and iscompliant with one or more of the following DSRC standards, includingany derivative or fork thereof: EN 12253:2004 Dedicated Short-RangeCommunication—Physical layer using microwave at 5.8 GHz (review); EN12795:2002 Dedicated Short-Range Communication (DSRC)—DSRC Data linklayer: Medium Access and Logical Link Control (review); EN 12834:2002Dedicated Short-Range Communication—Application layer (review); and EN13372:2004 Dedicated Short-Range Communication (DSRC)—DSRC profiles forRTTT applications (review); EN ISO 14906:2004 Electronic FeeCollection—Application interface.

A DSRC message is not any of the following: a WiFi message; a 3Gmessage; a 4G message; an LTE message; a millimeter wave communicationmessage; a Bluetooth message; a satellite communication; and ashort-range radio message transmitted or broadcast by a key fob at 315MHz or 433.92 MHz. For example, in the United States, key fobs forremote keyless systems include a short-range radio transmitter whichoperates at 315 MHz, and transmissions or broadcasts from thisshort-range radio transmitter are not DSRC messages since, for example,such transmissions or broadcasts do not comply with any DSRC standard,are not transmitted by a DSRC transmitter of a DSRC radio and are nottransmitted at 5.9 GHz. In another example, in Europe and Asia, key fobsfor remote keyless systems include a short-range radio transmitter whichoperates at 433.92 MHz, and transmissions or broadcasts from thisshort-range radio transmitter are not DSRC messages for similar reasonsas those described above for remote keyless systems in the UnitedStates.

In some embodiments, a DSRC-equipped device (e.g., a DSRC-equippedvehicle) does not include a conventional global positioning system unit(“GPS unit”), and instead includes a DSRC-compliant GPS unit. Aconventional GPS unit provides positional information that describes aposition of the conventional GPS unit with an accuracy of plus or minus10 meters of the actual position of the conventional GPS unit. Bycomparison, a DSRC-compliant GPS unit provides GPS data that describes aposition of the DSRC-compliant GPS unit with an accuracy of plus orminus 1.5 meters of the actual position of the DSRC-compliant GPS unit.This degree of accuracy is referred to as “lane-level accuracy” since,for example, a lane of a roadway is generally about 3 meters wide, andan accuracy of plus or minus 1.5 meters is sufficient to identify whichlane a vehicle is traveling in even when the roadway has more than onelanes of travel each heading in a same direction.

In some embodiments, a DSRC-compliant GPS unit is operable to identify,monitor and track its two-dimensional position within 1.5 meters, in alldirections, of its actual position 68% of the time under an open sky.

Embodiments of the allocation system are now described. Referring now toFIG. 1, depicted is a block diagram illustrating an operatingenvironment 100 for an allocation system 199 according to someembodiments. The operating environment 100 is present in a geographicregion so that each of the elements of the operating environment 100 ispresent in the same geographic region.

The operating environment 100 may include one or more of the followingelements: an ego vehicle 123 (referred to herein as a “vehicle 123” oran “ego vehicle 123”); a roadside device 103; an Nth remote vehicle 124(where “N” refers to any positive whole number greater than one); and acloud server 102. These elements of the operating environment 100 aredepicted by way of illustration. In practice, the operating environment100 may include one or more of the elements depicted in FIG. 1. The Nthremote vehicle 124 may be referred to as a remote vehicle 124.

In some embodiments, the ego vehicle 123, the remote vehicle 124, thenetwork 105, and the roadside device 103 may be elements of a vehicularmicro cloud 194. The cloud server 102 is not an element of the vehicularmicro cloud 194. The cloud server 102 and the roadside device 103 aredepicted in FIG. 1 with a dashed line to indicate that they are optionalfeatures of the operating environment 100.

In the depicted embodiment, the ego vehicle 123, the remote vehicle 124,and the roadside device 103 include similar elements. For example, eachof these elements of the operating environment 100 include their ownprocessor 125, bus 121, memory 127, communication unit 145, processor125, sensor set 126, and allocation system 199. These elements of theego vehicle 123, the remote vehicle 124, and the roadside device 103provide the same or similar functionality relative to one another.Accordingly, these descriptions will not be repeated in thisdescription.

In the depicted embodiment, the ego vehicle 123, remote vehicle 124, andthe roadside device 103 may each store similar digital data.

The vehicular micro cloud 194 may be a stationary vehicular micro cloudsuch as described by U.S. patent application Ser. No. 15/799,964 filedon Oct. 31, 2017 and entitled “Identifying a Geographic Location for aStationary Micro-Vehicular Cloud,” the entirety of which is hereinincorporated by reference. In this patent application the vehicularmicro cloud 194 may be a stationary vehicular micro cloud or a mobilevehicular micro cloud. Each of the ego vehicle 123, roadside device 103and the remote vehicle 124 are vehicular micro cloud members becausethey are connected endpoints that are members of the vehicular microcloud 194 that can access and use the unused computing resources (e.g.,their unused processing power, unused data storage, unused sensorcapabilities, unused bandwidth, etc.) of the other vehicular micro cloudmembers using wireless communications that are transmitted via thenetwork 105.

In some embodiments, the vehicular micro cloud 194 is a vehicular microcloud such as the one described in U.S. patent application Ser. No.15/799,963.

In some embodiments, a vehicular micro cloud 194 is not a V2X network ora V2V network because, for example, such networks do not includeallowing endpoints of such networks to access and use the unusedcomputing resources of the other endpoints of such networks. Bycomparison, a vehicular micro cloud 194 requires allowing all members ofthe vehicular micro cloud 194 to access and use designated unusedcomputing resources of the other members of the vehicular micro cloud194. In some embodiments, endpoints must satisfy a threshold of unusedcomputing resources in order to join the vehicular micro cloud 194. Theleader vehicle of the vehicular micro cloud 194 executes a process to:(1) determine whether endpoints satisfy the threshold as a condition forjoining the vehicular micro cloud 194; and (2) determine whether theendpoints that do join the vehicular micro cloud 194 continue to satisfythe threshold after they join as a condition for continuing to bemembers of the vehicular micro cloud 194.

In some embodiments, a member of the vehicular micro cloud 194 includesany endpoint (e.g., the ego vehicle 123, the remote vehicle 124, theroadside device 103, etc.) which has completed a process to join thevehicular micro cloud 194 (e.g., a handshake process with thecoordinator of the vehicular micro cloud 194). A member of the vehicularmicro cloud 194 is described herein as a “member” or a “micro cloudmember.” In some embodiments, the memory 127 of one or more of theendpoints stores member data. The member data is digital data thatdescribes one or more of the following: the identity of each of themicro cloud members; what digital data, or bits of data, are stored byeach micro cloud member; what computing services are available from eachmicro cloud member; what computing resources are available from eachmicro cloud member and what quantity of these resources are available;and how to communicate with each micro cloud member.

In some embodiments, the member data describes logical associationsbetween endpoints which are a necessary component of the vehicular microcloud 194 and serves to differentiate the vehicular micro cloud 194 froma mere V2X network. In some embodiments, a vehicular micro cloud 194must include a leader vehicle and this is a further differentiation froma vehicular micro cloud 194 and a V2X network.

The vehicular micro cloud 194 does not include a hardware server.Accordingly, the vehicular micro cloud 194 may be described asserverless.

The network 105 may be a conventional type, wired or wireless, and mayhave numerous different configurations including a star configuration,token ring configuration, or other configurations. Furthermore, thenetwork 105 may include a local area network (LAN), a wide area network(WAN) (e.g., the Internet), or other interconnected data paths acrosswhich multiple devices and/or entities may communicate. In someembodiments, the network 105 may include a peer-to-peer network. Thenetwork 105 may also be coupled to or may include portions of atelecommunications network for sending data in a variety of differentcommunication protocols. In some embodiments, the network 105 includesBluetooth® communication networks or a cellular communications networkfor sending and receiving data including via short messaging service(SMS), multimedia messaging service (MMS), hypertext transfer protocol(HTTP), direct data connection, wireless application protocol (WAP),e-mail, DSRC, full-duplex wireless communication, mmWave, WiFi(infrastructure mode), WiFi (ad-hoc mode), visible light communication,TV white space communication and satellite communication. The network105 may also include a mobile data network that may include 3G, 4G, LTE,LTE-V2X, LTE-D2D, VoLTE or any other mobile data network or combinationof mobile data networks. Further, the network 105 may include one ormore IEEE 802.11 wireless networks.

In some embodiments, the network 105 is a V2X network. For example, thenetwork 105 must include a vehicle, such as the ego vehicle 123, as anoriginating endpoint for each wireless communication transmitted by thenetwork 105. An originating endpoint is the endpoint that initiated awireless communication using the network 105. In some embodiments, thenetwork 105 is a vehicular network.

The network 105 is an element of the vehicular micro cloud 194.Accordingly, the vehicular micro cloud 194 is not the same thing as thenetwork 105 since the network is merely a component of the vehicularmicro cloud 194. For example, the network 105 does not include memberdata. The network 105 also does not include a leader vehicle.

In some embodiments, one or more of the ego vehicle 123 and the remotevehicle 124 are DSRC-equipped vehicles. In some embodiments, theroadside device 103 is a DSRC-equipped device. For example, the egovehicle 123 includes a DSRC-compliant GPS unit 150 and a DSRC radio(e.g., the V2X radio 144 is a DSRC radio in embodiments where the egovehicle 123 is a DSRC-equipped vehicle) and the roadside device 103includes a communication unit 145 having a DSRC radio similar to the oneincluded in the ego vehicle 123. The network 105 may include a DSRCcommunication channel shared among the ego vehicle 123 and a secondvehicle.

The ego vehicle 123 may include a car, a truck, a sports utilityvehicle, a bus, a semi-truck, a drone, or any other roadway-basedconveyance. In some embodiments, the ego vehicle 123 may include anautonomous vehicle or a semi-autonomous vehicle. Although not depictedin FIG. 1, in some embodiments, the ego vehicle 123 includes anautonomous driving system. The autonomous driving system includes codeand routines that provides sufficient autonomous driving features to theego vehicle 123 to render the ego vehicle 123 an autonomous vehicle or ahighly autonomous vehicle. In some embodiments, the ego vehicle 123 is aLevel III autonomous vehicle or higher as defined by the NationalHighway Traffic Safety Administration and the Society of AutomotiveEngineers.

The ego vehicle 123 is a connected vehicle. For example, the ego vehicle123 is communicatively coupled to the network 105 and operable to sendand receive messages via the network 105.

The ego vehicle 123 includes one or more of the following elements: aprocessor 125; a sensor set 126; a DSRC-compliant GPS unit 150; acommunication unit 145; an onboard unit 139; a memory 127; and anallocation system 199. These elements may be communicatively coupled toone another via a bus 121.

The processor 125 includes an arithmetic logic unit, a microprocessor, ageneral-purpose controller, or some other processor array to performcomputations and provide electronic display signals to a display device.The processor 125 processes data signals and may include variouscomputing architectures including a complex instruction set computer(CISC) architecture, a reduced instruction set computer (RISC)architecture, or an architecture implementing a combination ofinstruction sets. Although FIG. 1 depicts a single processor 125 presentin the ego vehicle 123, multiple processors may be included in the egovehicle 123. The processor 125 may include a graphical processing unit.Other processors, operating systems, sensors, displays, and physicalconfigurations may be possible.

In some embodiments, the processor 125 may be an element of aprocessor-based computing device of the ego vehicle 123. For example,the ego vehicle 123 may include one or more of the followingprocessor-based computing devices and the processor 125 may be anelement of one of these devices: an onboard vehicle computer; anelectronic control unit; a navigation system; an advanced driverassistance system (“ADAS system”) and a head unit. In some embodiments,the processor 125 is an element of the onboard unit 139.

The onboard unit 139 is a special purpose processor-based computingdevice. In some embodiments, the onboard unit 139 is a communicationdevice that includes one or more of the following elements: thecommunication unit 145; the processor 125; the memory 127; and theallocation system 199. In some embodiments, the onboard unit 139 is thecomputer system 200 depicted in FIG. 2. In some embodiments, the onboardunit 139 is an electronic control unit (ECU).

The sensor set 126 includes one or more onboard sensors. The sensor set126 may record sensor measurements that describe the ego vehicle 123 orthe physical environment that includes the ego vehicle 123. The sensordata 191 includes digital data that describes the sensor measurements.

In some embodiments, the sensor set 126 may include one or more sensorsthat are operable to measure the physical environment outside of the egovehicle 123. For example, the sensor set 126 may include cameras, lidar,radar, sonar and other sensors that record one or more physicalcharacteristics of the physical environment that is proximate to the egovehicle 123.

In some embodiments, the sensor set 126 may include one or more sensorsthat are operable to measure the physical environment inside a cabin ofthe ego vehicle 123. For example, the sensor set 126 may record an eyegaze of the driver (e.g., using an internal camera), where the driver'shands are located (e.g., using an internal camera) and whether thedriver is touching a head unit or infotainment system with their hands(e.g., using a feedback loop from the head unit or infotainment systemthat indicates whether the buttons, knobs or screen of these devices isbeing engaged by the driver).

In some embodiments, the sensor set 126 may include one or more of thefollowing sensors: an altimeter; a gyroscope; a proximity sensor; amicrophone; a microphone array; an accelerometer; a camera (internal orexternal); a LIDAR sensor; a laser altimeter; a navigation sensor (e.g.,a global positioning system sensor of the DSRC-compliant GPS unit 150);an infrared detector; a motion detector; a thermostat; a sound detector,a carbon monoxide sensor; a carbon dioxide sensor; an oxygen sensor; amass air flow sensor; an engine coolant temperature sensor; a throttleposition sensor; a crank shaft position sensor; an automobile enginesensor; a valve timer; an air-fuel ratio meter; a blind spot meter; acurb feeler; a defect detector; a Hall effect sensor, a manifoldabsolute pressure sensor; a parking sensor; a radar gun; a speedometer;a speed sensor; a tire-pressure monitoring sensor; a torque sensor; atransmission fluid temperature sensor; a turbine speed sensor (TSS); avariable reluctance sensor; a vehicle speed sensor (VSS); a watersensor; a wheel speed sensor; and any other type of automotive sensor.

The sensor set 126 may be operable to record sensor data 191 thatdescribes images or other measurements of the physical environment andobjects or other vehicles present in the roadway environment such aspedestrians, animals, traffic signs, traffic lights, potholes, etc.

The physical environment may include a roadway region, parking lot, orparking garage that is proximate to the ego vehicle 123. The sensor data191 may describe measurable aspects of the physical environment.

In some embodiments, the sensors of the sensor set 126 are operable tocollect sensor data 191. The sensors of the sensor set 126 include anysensors that are necessary to measure and record the measurementsdescribed by the sensor data 191. In some embodiments, the sensor data191 includes any measurements that are necessary to generate the otherdigital data stored by the memory 127. For example, the sensor data 191describes the measurements necessary to generate the feature data 193.

In some embodiments, the sensor data 191 describes any of theinformation that is included in the V2X data 192. In some embodiments,the sensor set 126 includes any sensors that are necessary to record theinformation that is included in the V2X data 192.

The V2X data 192 is digital data that describes information included asthe payload for a V2X message. In some embodiments, the V2X data 192 isthe payload for a DSRC message or any other type of V2X message. In someembodiments, the ego vehicle 123 generates its own V2X data 192 usingits own sensor data 191 and transmits V2X messages including this V2Xdata 192 as its payload. In some embodiments, the ego vehicle 123 usesthe V2X radio 144 to broadcast DSRC messages including this V2X data 192as its payload. In this way, other vehicles (such as the remote vehicle124) may learn of the sensor measurements generated by the ego vehicle123. These other vehicles also execute this process so that the egovehicle 123 receives their sensor measurements.

In some embodiments, the DSRC messages (or V2X messages including V2Xdata 192) may be treated as a form of feedback that: confirms theaccuracy of a vehicle's own sensor measurements; is used to improve theaccuracy of these sensor measurements; or is used as an input to alearning algorithm that improves the accuracy of a vehicle's sensormeasurements over time based on the feedback received from othervehicles.

In some embodiments, the DSRC-compliant GPS unit 150 includes anyhardware and software necessary to make the ego vehicle 123 or theDSRC-compliant GPS unit 150 compliant with one or more of the followingDSRC standards, including any derivative or fork thereof: EN 12253:2004Dedicated Short-Range Communication—Physical layer using microwave at5.8 GHz (review); EN 12795:2002 Dedicated Short-Range Communication(DSRC)—DSRC Data link layer: Medium Access and Logical Link Control(review); EN 12834:2002 Dedicated Short-Range Communication—Applicationlayer (review); and EN 13372:2004 Dedicated Short-Range Communication(DSRC)—DSRC profiles for RTTT applications (review); EN ISO 14906:2004Electronic Fee Collection—Application interface.

In some embodiments, the DSRC-compliant GPS unit 150 is operable toprovide GPS data describing the location of the ego vehicle 123 withlane-level accuracy. For example, the ego vehicle 123 is traveling in alane of a multi-lane roadway. Lane-level accuracy means that the lane ofthe ego vehicle 123 is described by the GPS data so accurately that aprecise lane of travel of the vehicle 123 may be accurately determinedbased on the GPS data for this vehicle 123 as provided by theDSRC-compliant GPS unit 150.

An example process for generating GPS data describing a geographiclocation of an object (e.g., the ego vehicle 123, the remote vehicle124, or some other object located in a roadway environment) is nowdescribed according to some embodiments. In some embodiments, the sensorsystem 199 include code and routines that are operable, when executed bythe processor 125, to cause the processor to: analyze (1) GPS datadescribing the geographic location of the ego vehicle 123 and (2) sensordata 191 describing the range separating the ego vehicle 123 from anobject and a heading for this range; and determine, based on thisanalysis, GPS data describing the location of the object. The GPS datadescribing the location of the object may also have lane-level accuracybecause, for example, it is generated using accurate GPS data of the egovehicle 123 and accurate sensor data describing information about theobject.

In some embodiments, the DSRC-compliant GPS unit 150 includes hardwarethat wirelessly communicates with a GPS satellite (or GPS server) toretrieve GPS data that describes the geographic location of the egovehicle 123 with a precision that is compliant with the DSRC standard.The DSRC standard requires that GPS data be precise enough to infer iftwo vehicles (one of which is, for example, the ego vehicle 123) arelocated in adjacent lanes of travel on a roadway. In some embodiments,the DSRC-compliant GPS unit 150 is operable to identify, monitor andtrack its two-dimensional position within 1.5 meters of its actualposition 68% of the time under an open sky. Since roadway lanes aretypically no less than 3 meters wide, whenever the two-dimensional errorof the GPS data is less than 1.5 meters the allocation system 199described herein may analyze the GPS data provided by the DSRC-compliantGPS unit 150 and determine what lane the ego vehicle 123 is traveling inbased on the relative positions of two or more different vehicles (oneof which is, for example, the ego vehicle 123) traveling on a roadway atthe same time.

By comparison to the DSRC-compliant GPS unit 150, a conventional GPSunit which is not compliant with the DSRC standard is unable todetermine the location of a vehicle 123 with lane-level accuracy. Forexample, a typical parking space is approximately 3 meters wide.However, a conventional GPS unit only has an accuracy of plus or minus10 meters relative to the actual location of the ego vehicle 123. As aresult, such conventional GPS units are not sufficiently accurate toenable the allocation system 199 to determine the lane of travel of theego vehicle 123. This measurement improves the accuracy of the GPS datadescribing the location of parking spaces used by the allocation system199 when providing its functionality.

In some embodiments, the memory 127 stores two types of GPS data. Thefirst is GPS data of the ego vehicle 123 and the second is GPS data ofone or more objects (e.g., the remote vehicle 124 or some other objectin the roadway environment). The GPS data of the ego vehicle 123 isdigital data that describes a geographic location of the ego vehicle123. The GPS data of the parking space is digital data that describes ageographic location of an object. One or more of these two types of GPSdata may have lane-level accuracy. In some embodiments, one or more ofthese two types of GPS data are described by the sensor data 191.

The communication unit 145 transmits and receives data to and from anetwork 105 or to another communication channel. In some embodiments,the communication unit 145 may include a DSRC transmitter, a DSRCreceiver and other hardware or software necessary to make the egovehicle 123 a DSRC-equipped device.

In some embodiments, the communication unit 145 includes a port fordirect physical connection to the network 105 or to anothercommunication channel. For example, the communication unit 145 includesa USB, SD, CAT-5, or similar port for wired communication with thenetwork 105. In some embodiments, the communication unit 145 includes awireless transceiver for exchanging data with the network 105 or othercommunication channels using one or more wireless communication methods,including: IEEE 802.11; IEEE 802.16, BLUETOOTH®; EN ISO 14906:2004Electronic Fee Collection—Application interface EN 11253:2004 DedicatedShort-Range Communication—Physical layer using microwave at 5.8 GHz(review); EN 12795:2002 Dedicated Short-Range Communication (DSRC)—DSRCData link layer: Medium Access and Logical Link Control (review); EN12834:2002 Dedicated Short-Range Communication—Application layer(review); EN 13372:2004 Dedicated Short-Range Communication (DSRC)—DSRCprofiles for RTTT applications (review); the communication methoddescribed in U.S. patent application Ser. No. 14/471,387 filed on Aug.28, 2014 and entitled “Full-Duplex Coordination System”; or anothersuitable wireless communication method.

In some embodiments, the communication unit 145 includes a full-duplexcoordination system as described in U.S. patent application Ser. No.14/471,387 filed on Aug. 28, 2014 and entitled “Full-Duplex CoordinationSystem,” the entirety of which is incorporated herein by reference.

In some embodiments, the communication unit 145 includes a cellularcommunications transceiver for sending and receiving data over acellular communications network including via short messaging service(SMS), multimedia messaging service (MMS), hypertext transfer protocol(HTTP), direct data connection, WAP, e-mail, or another suitable type ofelectronic communication. In some embodiments, the communication unit145 includes a wired port and a wireless transceiver. The communicationunit 145 also provides other conventional connections to the network 105for distribution of files or media objects using standard networkprotocols including TCP/IP, HTTP, HTTPS, and SMTP, millimeter wave,DSRC, etc.

In some embodiments, the communication unit 145 includes a V2X radio144. The V2X radio 144 is a hardware unit that includes one or moretransmitters and one or more receivers that is operable to send andreceive any type of V2X message.

In some embodiments, the V2X radio 144 includes a DSRC transmitter and aDSRC receiver. The DSRC transmitter is operable to transmit andbroadcast DSRC messages over the 5.9 GHz band. The DSRC receiver isoperable to receive DSRC messages over the 5.9 GHz band. In someembodiments, the DSRC transmitter and the DSRC receiver operate on someother band which is reserved exclusively for DSRC.

In some embodiments, the V2X radio 144 includes a non-transitory memorywhich stores digital data that controls the frequency for broadcastingBasic Safety Message (“BSM message” if singular, or “BSM messages” ifplural). In some embodiments, the non-transitory memory stores abuffered version of the GPS data for the ego vehicle 123 so that the GPSdata for the ego vehicle 123 is broadcast as an element of the BSMmessages which are regularly broadcast by the V2X radio 144 (e.g., at aninterval of once every 0.10 seconds).

In some embodiments, the V2X radio 144 includes any hardware or softwarewhich is necessary to make the ego vehicle 123 compliant with the DSRCstandards. In some embodiments, the DSRC-compliant GPS unit 150 is anelement of the V2X radio 144.

The memory 127 may include a non-transitory storage medium. The memory127 may store instructions or data that may be executed by the processor125. The instructions or data may include code for performing thetechniques described herein. The memory 127 may be a dynamicrandom-access memory (DRAM) device, a static random-access memory (SRAM)device, flash memory, or some other memory device. In some embodiments,the memory 127 also includes a non-volatile memory or similar permanentstorage device and media including a hard disk drive, a floppy diskdrive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RWdevice, a flash memory device, or some other mass storage device forstoring information on a more permanent basis.

In some embodiments, the memory 127 may store any or all of the digitaldata or information described herein.

As depicted in FIG. 1, the memory 127 stores the following digital data:the sensor data 191; the V2X data 192; the feature data 193; profiledata 194; prediction data 195; schedule data 197; task data 198; andpredicted resource availability data 189.

The sensor data 191 is digital data that describes the environment ofthe connected vehicle. The sensor data 191 describes the measurements ofthe sensors included in the sensor set 126. In some embodiments, theallocation system 199 includes code and routines that are operable, whenexecuted by the processor 125, to cause the processor 125 to: execute oractivate one or more sensors of the sensor set 126 to cause to recordthe sensor measurements that are described by the sensor data 195; andstore these sensor measurements as the sensor data 195 in the memory127.

The V2X data 192 is digital data that describes the payload for a DSRCmessage or some other V2X message transmitted or received by the egovehicle 123. In some embodiments, the allocation system 199 includescode and routines that are operable, when executed by the processor 125,to cause the processor 125 to: analyze the sensor data 191; generate thefeature data 193 based on the sensor data 191; generate the V2X data 192so that it includes the feature data 193 as a component of the V2X data192 (e.g., part 2 of a BSM message); and store the V2X data 192 in thememory 127 or as the payload for a DSRC message or some other type ofV2X message to be transmitted by the communication unit 145 to anothermember.

In some embodiments, the V2X data 192 is the payload for a V2X messagethat is received by the ego vehicle 123 where this V2X message waspreviously transmitted by one or more DSRC-enabled vehicles. Forexample, the V2X message is a DSRC message such as a BSM message. Insome embodiments, each member of the vehicular micro cloud 194broadcasts V2X messages that include the profile data 195 that eachmember generates before joining the vehicular micro cloud 194. If theV2X message is a BSM message, then the profile data 195 is an element ofpart 2 of the BSM data included in the payload for the BSM message.

The feature data 193 is digital data that describes a mobility featurevector. In some embodiments, the allocation system 199 generates featuredata based on the sensor data 191 and/or the V2X data 192 (e.g., the V2Xdata 192 received from other members). An example mobility featurevector includes mi(t) where mi describes the sensor measurements fromthe sensor data 191 recorded by the ego vehicle 123 at different times“t” and the V2X data 192 which include the sensor data 191 of othermembers as recorded by these other members at different times “t.”

In some embodiments, the allocation system 199 periodically measures itsown resource availability and its own mobility feature vector. Byanalyzing the long-term history of these <resource, mobilityfeature>pairs, the allocation system 199 can compose a “resourceprofile” function which allows to estimate available resources based onthe mobility feature at a given point in time. This process is executedby individual connected vehicles (e.g., the ego vehicle 123 and theremote vehicle 124) before a vehicular micro cloud 194 is formed.

The profile data 194 is digital data that describes a resource profilefor the ego vehicle 123. An example resource profile is fi(mi), which isa function of the mobility feature vector. In some embodiments, the egovehicle uses a regression technique to compose the function fi(mi) thatmodels relationship between the mobility feature vector mi and thecomputing resources available on the ego vehicle, assuming that theavailable computing resources have correlation with the mobility featurevector. In some embodiments, each of the members of the vehicular microcloud 194 include an allocation system 199 which execute steps 1-4 ofthe example general method described above so that each member generatesprofile data 195 describing itself. The profile data 195 may be includedin the V2X data 192 which each member broadcasts to the other memberswhen they broadcast V2X messages.

The prediction data 196 is digital data that describes the predictedfuture behavior of the ego vehicle 123 (or the remote vehicle 124 if theremote vehicle 124 is the element which generates the prediction data196). In some embodiments, the prediction described by the predictiondata is a short-term prediction. An example short-term predictionincludes mi(t+Δt).

The schedule data 197 is digital data that describes which membervehicles should execute which tasks or sub-tasks so that a computationaltask is completed.

The task data 198 is digital data that describes a set of computationaltasks (“tasks”) that are to be completed by the vehicular micro cloud194. Each computational task may be subdivided into a set of sub-tasks.In some embodiments, the task data 198 is transmitted to the ego vehicle123 by the cloud server 102 via the network 105. The task data 198 mayinclude digital data that describes a task to be completed.

The predicted resource availability data 189 is digital data thatdescribes a predicted resource availability for the ego vehicle and theother members of the vehicular micro cloud 194 based on the resourceprofiles described by the profile data 195. Because each memberbroadcasts its profile data 195, each member of the vehicular microcloud 194 stores the profile data 195 for each of the other members. Anexample of predicted resource availability data for a particular vehicleis described by Ri(t)=fi(mi(t+Δt)).

In some embodiments, the allocation system 199 includes code androutines that are operable, when executed by the processor 125, toexecute one or more steps of one or more of the method 300 describedherein with reference to FIG. 3. In some embodiments, the allocationsystem 199 includes code and routines that are operable, when executedby the processor 125, to execute one or more steps of the examplegeneral method described above.

As depicted, the allocation system 199 includes the following elements:mobility predictor 140; a resource profile 141; and a task scheduler142.

In some embodiments, the mobility predictor 140 includes code androutines that are operable, when executed by the processor 125, to causethe processor 125 to execute the steps that generate the prediction data196.

In some embodiments, the resource profiler 141 includes code androutines that are operable, when executed by the processor 125, to causethe processor 125 to execute the steps that generate the profile data195 and/or the predicted resource availability data 189.

In some embodiments, the task scheduler 142 includes code and routinesthat are operable, when executed by the processor 125, to cause theprocessor 125 to execute the steps that generate the task data 198 orthe schedule data 197.

In some embodiments, the allocation system 199 is an element of theonboard unit 139 or some other onboard vehicle computer.

In some embodiments, the allocation system 199 is implemented usinghardware including a field-programmable gate array (“FPGA”) or anapplication-specific integrated circuit (“ASIC”). In some otherembodiments, the allocation system 199 is implemented using acombination of hardware and software.

In some embodiments, the roadside device 103 is a device that (1)includes a communication unit 145 and a processor 125 and (2) is presentin an environment (e.g., a roadway environment) with the ego vehicle123. For example, the roadside device 103 is a roadside unit (RSU) orsome other infrastructure device including the communication unit 145and the processor 125 and present in the same environment as the egovehicle 123.

As depicted, the roadside device 103 includes the following elements: amemory 127; a bus 121; a processor 125; a communication unit 145; asensor set 126; and an allocation system 199. These elements of theroadside device 103 provide similar functionality as those describedabove for the ego vehicle 123, and so, these descriptions will not berepeated here.

In some embodiments, the roadside device 103 is not an element of thevehicular micro cloud 194. In some embodiments, the roadside device 103does not include a server.

The remote vehicle 124 includes elements and functionality which aresimilar to those described above for the ego vehicle 123, and so, thosedescriptions will not be repeated here. In some embodiments, the egovehicle 123 and the remote vehicle 124 are located in a geographicregion which is managed by the roadside device 103. For example, theroadside device 103 is a stationary connected device that is responsiblefor establishing and maintaining stationary vehicular micro clouds at aparticular geographic location or within a particular geographic regionthat includes the geographic locations described by the GPS data of theego vehicle 123, the remote vehicle 124, and the roadside device 103.

The cloud server 102 is a connected processor-based computing devicethat is not a member of the vehicular micro cloud 194 and includes aninstance of the allocation system 199 and a non-transitory memory (notpictured) that stores at least one instance of the task data 198. Forexample, the cloud server 102 is one or more of the following: ahardware server; a personal computer; a laptop; a device such as theroadside device 103 which is not a member of the vehicular micro cloud194; or any other processor-based connected device that is not a memberof the vehicular micro cloud 194 and includes an instance of theallocation system 199 and a non-transitory memory that stores at leastone instance of task data 198. The cloud server 102 may include abackbone network.

In some embodiments, the vehicular micro cloud 194 is stationary. Inother words, in some embodiments the vehicular micro cloud 194 is a“stationary vehicular micro cloud.” A stationary vehicular micro cloudis a wireless network system in which a plurality of connected vehicles(such as the ego vehicle 123 and the remote vehicle 124), and optionallydevices such as the roadside device 103, form a cluster ofinterconnected vehicles that are located at a same geographic region.These connected vehicles (and, optionally, connected devices) areinterconnected via Wi-Fi, mmWave, DSRC or some other form of V2Xwireless communication. For example, the connected vehicles areinterconnected via a V2X network which may be the network 105 or someother wireless network that is only accessed by the members of thevehicular micro cloud 194 and not non-members such as the cloud server102. Connected vehicles (and devices such as the roadside device 103)which are members of the same stationary vehicular micro cloud maketheir unused computing resources available to the other members of thestationary vehicular micro cloud.

In some embodiments, the vehicular micro cloud 194 is “stationary”because the geographic location of the vehicular micro cloud 194 isstatic; different vehicles constantly enter and exit the vehicular microcloud 194 over time. This means that the computing resources availablewithin the vehicular micro cloud 194 is variable based on the trafficpatterns for the geographic location at different times of day:increased traffic corresponds to increased computing resources becausemore vehicles will be eligible to join the vehicular micro cloud 194;and decreased traffic corresponds to decreased computing resourcesbecause less vehicles will be eligible to join the vehicular micro cloud194.

In some embodiments, the V2X network is a non-infrastructure network. Anon-infrastructure network is any conventional wireless network thatdoes not include infrastructure such as cellular towers, servers, orserver farms. For example, the V2X network specifically does not includea mobile data network including third-generation (3G), fourth-generation(4G), fifth-generation (5G), long-term evolution (LTE), Voice-over-LTE(VoLTE) or any other mobile data network that relies on infrastructuresuch as cellular towers, hardware servers or server farms.

In some embodiments, the non-infrastructure network includes Bluetooth®communication networks for sending and receiving data including via oneor more of DSRC, mmWave, full-duplex wireless communication and anyother type of wireless communication that does not includeinfrastructure elements. The non-infrastructure network may includevehicle-to-vehicle communication such as a Wi-Fi™ network shared amongtwo or more vehicles 123, 124.

In some embodiments, the wireless messages described herein may beencrypted themselves or transmitted via an encrypted communicationprovided by the network 105. In some embodiments, the network 105 mayinclude an encrypted virtual private network tunnel (“VPN tunnel”) thatdoes not include any infrastructure components such as network towers,hardware servers or server farms. In some embodiments, the allocationsystem 199 includes encryption keys for encrypting wireless messages anddecrypting the wireless messages described herein.

Referring now to FIG. 2, depicted is a block diagram illustrating anexample computer system 200 including an allocation system 199 accordingto some embodiments.

In some embodiments, the computer system 200 may include aspecial-purpose computer system that is programmed to perform one ormore steps of one or more of the method 300 described herein withreference to FIG. 3. In some embodiments, the computer system 200 mayinclude a special-purpose computer system that is programmed to performone or more steps of one or more of the example general method describedabove.

In some embodiments, the computer system 200 may include aprocessor-based computing device. For example, the computer system 200may include an onboard vehicle computer system of the ego vehicle 123 orthe remote vehicle 124; the computer system 200 may also include anonboard computer system of the roadside device 103.

The computer system 200 may include one or more of the followingelements according to some examples: the allocation system 199; aprocessor 125; a communication unit 145; a DSRC-compliant GPS unit 150;a storage 241; and a memory 127. The components of the computer system200 are communicatively coupled by a bus 220.

In the illustrated embodiment, the processor 125 is communicativelycoupled to the bus 220 via a signal line 237. The communication unit 145is communicatively coupled to the bus 220 via a signal line 246. TheDSRC-compliant GPS unit 150 is communicatively coupled to the bus 220via a signal line 247. The storage 241 is communicatively coupled to thebus 220 via a signal line 242. The memory 127 is communicatively coupledto the bus 220 via a signal line 244.

The following elements of the computer system 200 were described abovewith reference to FIG. 1, and so, these descriptions will not berepeated here: the processor 125; the communication unit 145; theDSRC-compliant GPS unit 150; and the memory 127.

The storage 241 can be a non-transitory storage medium that stores datafor providing the functionality described herein. The storage 241 may bea DRAM device, a SRAM device, flash memory, or some other memorydevices. In some embodiments, the storage 241 also includes anon-volatile memory or similar permanent storage device and mediaincluding a hard disk drive, a floppy disk drive, a CD-ROM device, aDVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memorydevice, or some other mass storage device for storing information on amore permanent basis.

In some embodiments, the allocation system 199 includes code androutines that are operable, when executed by the processor 125, to causethe processor 125 to execute one or more steps of the method 300described herein with reference to FIG. 3. In some embodiments, theallocation system 199 includes code and routines that are operable, whenexecuted by the processor 125, to cause the processor 125 to execute oneor more steps of the example general method described above.

In the illustrated embodiment shown in FIG. 2, the allocation system 199includes a communication module 202.

The communication module 202 can be software including routines forhandling communications between the allocation system 199 and othercomponents of the computer system 200. In some embodiments, thecommunication module 202 can be a set of instructions executable by theprocessor 125 to provide the functionality described below for handlingcommunications between the allocation system 199 and other components ofthe computer system 200. In some embodiments, the communication module202 can be stored in the memory 127 of the computer system 200 and canbe accessible and executable by the processor 125. The communicationmodule 202 may be adapted for cooperation and communication with theprocessor 125 and other components of the computer system 200 via signalline 222.

The communication module 202 sends and receives data, via thecommunication unit 145, to and from one or more elements of theoperating environment 100.

In some embodiments, the communication module 202 receives data fromcomponents of the allocation system 199 and stores the data in one ormore of the storage 241 and the memory 127.

In some embodiments, the communication module 202 may handlecommunications between components of the allocation system 199 or thecomputer system 200.

Referring now to FIG. 3, depicted is a flowchart of an example method300. The method 300 includes steps 302, 304, and 306 as depicted in FIG.3. The steps of the method 300 may be executed in any order, and notnecessarily those depicted in FIG. 3. In some embodiments, one or moreof the steps are skipped or modified in ways that are described hereinor known or otherwise determinable by those having ordinary skill in theart of vehicular micro clouds.

In the above description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofthe specification. It will be apparent, however, to one skilled in theart that the disclosure can be practiced without these specific details.In some instances, structures and devices are shown in block diagramform in order to avoid obscuring the description. For example, thepresent embodiments can be described above primarily with reference touser interfaces and particular hardware. However, the presentembodiments can apply to any type of computer system that can receivedata and commands, and any peripheral devices providing services.

Reference in the specification to “some embodiments” or “some instances”means that a particular feature, structure, or characteristic describedin connection with the embodiments or instances can be included in atleast one embodiment of the description. The appearances of the phrase“in some embodiments” in various places in the specification are notnecessarily all referring to the same embodiments.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms including “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission, or display devices.

The present embodiments of the specification can also relate to anapparatus for performing the operations herein. This apparatus may bespecially constructed for the required purposes, or it may include ageneral-purpose computer selectively activated or reconfigured by acomputer program stored in the computer. Such a computer program may bestored in a computer-readable storage medium, including, but is notlimited to, any type of disk including floppy disks, optical disks,CD-ROMs, and magnetic disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, flashmemories including USB keys with non-volatile memory, or any type ofmedia suitable for storing electronic instructions, each coupled to acomputer system bus.

The specification can take the form of some entirely hardwareembodiments, some entirely software embodiments or some embodimentscontaining both hardware and software elements. In some preferredembodiments, the specification is implemented in software, whichincludes, but is not limited to, firmware, resident software, microcode,etc.

Furthermore, the description can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer-readable medium can be any apparatus thatcan contain, store, communicate, propagate, or transport the program foruse by or in connection with the instruction execution system,apparatus, or device.

A data processing system suitable for storing or executing program codewill include at least one processor coupled directly or indirectly tomemory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including, but not limited, to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem, and Ethernet cards are just a few of thecurrently available types of network adapters.

Finally, the algorithms and displays presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may be used with programs in accordance with theteachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these systems will appear from thedescription below. In addition, the specification is not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the specification as described herein.

The foregoing description of the embodiments of the specification hasbeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the specification to theprecise form disclosed. Many modifications and variations are possiblein light of the above teaching. It is intended that the scope of thedisclosure be limited not by this detailed description, but rather bythe claims of this application. As will be understood by those familiarwith the art, the specification may be embodied in other specific formswithout departing from the spirit or essential characteristics thereof.Likewise, the particular naming and division of the modules, routines,features, attributes, methodologies, and other aspects are not mandatoryor significant, and the mechanisms that implement the specification orits features may have different names, divisions, or formats.Furthermore, as will be apparent to one of ordinary skill in therelevant art, the modules, routines, features, attributes,methodologies, and other aspects of the disclosure can be implemented assoftware, hardware, firmware, or any combination of the three. Also,wherever a component, an example of which is a module, of thespecification is implemented as software, the component can beimplemented as a standalone program, as part of a larger program, as aplurality of separate programs, as a statically or dynamically linkedlibrary, as a kernel-loadable module, as a device driver, or in everyand any other way known now or in the future to those of ordinary skillin the art of computer programming. Additionally, the disclosure is inno way limited to embodiment in any specific programming language, orfor any specific operating system or environment. Accordingly, thedisclosure is intended to be illustrative, but not limiting, of thescope of the specification, which is set forth in the following claims.

What is claimed is:
 1. A computer program product for a vehicular micro cloud that includes a set of connected vehicles that are operable to collectively execute tasks which no single vehicle can execute due to computational limitations of the single vehicle, wherein the computer program product comprises a non-transitory memory storing computer-executable code that, when executed by a processor, causes the processor to: determine, for the vehicular micro cloud, a set of computing sub-tasks to be completed, wherein the vehicular micro cloud includes the set of connected vehicles that are members of the vehicular micro cloud and located in a similar geographic area; determine vehicle travel speeds for the members of the vehicular micro cloud; and assign the computing sub-tasks to the members based on the vehicle travel speeds of the members relative to one another so that the members that the computational sub-tasks are assigned to the members that are either stationary or traveling at the slowest vehicle travel speeds, wherein the computing sub-task is completed by the member to which it is assigned.
 2. The computer program product of claim 1, wherein the computer-executable code is collaboratively executed by two or more onboard vehicle computers of a plurality of the members of the vehicular micro cloud.
 3. The computer program product of claim 1, wherein at least one of the members is a leader vehicle that solely executes the computer program product and controls when other members are eligible to leave the vehicular micro cloud.
 4. The computer program product of claim 1, wherein at least one of the members is a leader vehicle that solely executes the computer program product and the leader vehicle is selected based on a set of factors that includes: unused processing power; sensor accuracy; unused bandwidth; and unused memory.
 5. The computer program product of claim 1, wherein the non-transitory memory stores additional computer-executable code that, when executed by the processor, causes the processor to: determine a complexity of the computing sub-tasks; determine a sub-task ranking for the computing sub-tasks based on their complexity, wherein higher sub-task rankings are assigned to the computing sub-tasks that are more complex; and determine a member ranking for the members based on their speeds, wherein higher member rankings are assigned to the members that are stationary or traveling at the slowest speeds, wherein the assigning of the computing sub-tasks to the members is based on the sub-task rankings and the member rankings so that higher ranked sub-tasks are assigned to be completed by higher ranked members.
 6. The computer program product of claim 5, wherein each of the computing sub-tasks is assigned to a member whose member ranking is the same as the sub-task ranking for computing sub-task.
 7. The computer program product of claim 5, wherein a member is assigned a plurality of sub-tasks.
 8. The computer program product of claim 7, wherein the plurality is executed in an order that is determined based on a set of factors that include increasing safety.
 9. A method comprising: determining, for a vehicular micro cloud, a set of computing sub-tasks to be completed, wherein the vehicular micro cloud includes a set of vehicles that are members of the vehicular micro cloud and located in a similar geographic area; determining vehicle travel speeds for the members of the vehicular micro cloud; and assigning the computing sub-tasks to the members based on the vehicle travel speeds of the members relative to one another so that the members that the computational sub-tasks are assigned to the members that are either stationary or traveling at the slowest vehicle travel speeds, wherein the computing sub-task is completed by the member to which it is assigned.
 10. The method of claim 9, wherein the steps of the method are executed by an onboard vehicle computer of an ego vehicle that is one of the members of the vehicular micro cloud.
 11. The method of claim 9, wherein at least one of the members is a leader vehicle that executes the method and controls when other members are eligible to leave the vehicular micro cloud.
 12. The method of claim 9, wherein at least one of the members is a leader vehicle that executes the method and the leader vehicle is selected based on a set of factors that includes: unused processing power; sensor accuracy; unused bandwidth; and unused memory.
 13. The method of claim 9, further comprising: determining a complexity of the computing sub-tasks; determining a sub-task ranking for the computing sub-tasks based on their complexity, wherein higher sub-task rankings are assigned to the computing sub-tasks that are more complex; and determining a member ranking for the members based on their speeds, wherein higher member rankings are assigned to the members that are stationary or traveling at the slowest speeds, wherein the assigning of the computing sub-tasks to the members is based on the sub-task rankings and the member rankings so that higher ranked sub-tasks are assigned to be completed by higher ranked members.
 14. The method of claim 13, wherein each of the computing sub-tasks is assigned to a member whose member ranking is the same as the sub-task ranking for computing sub-task.
 15. The method of claim 13, wherein a member is assigned a plurality of sub-tasks.
 16. The method of claim 15, wherein the plurality is executed in an order that is determined based on a set of factors that include increasing safety.
 17. A system comprising: an ego vehicle including a processor executing computer-executable code that is operable, when executed by the processor, to cause the processor to: determine, for a vehicular micro cloud, a set of computing sub-tasks to be completed, wherein the vehicular micro cloud includes a set of connected vehicles that are members of the vehicular micro cloud and located in a similar geographic area; determine vehicle travel speeds for the members of the vehicular micro cloud; and assign the computing sub-tasks to the members based on the vehicle travel speeds of the members relative to one another so that the members that the computational sub-tasks are assigned to the members that are either stationary or traveling at the slowest vehicle travel speeds, wherein the computing sub-task is completed by the member to which it is assigned.
 18. The system of claim 17, wherein at least one of the members is a leader vehicle that executes the computer-executable code and controls when other members are eligible to leave the vehicular micro cloud.
 19. The system of claim 17, wherein at least one of the members is a leader vehicle that executes the computer-executable code and the leader vehicle is dynamically selected based on a set of factors that includes: unused processing power; sensor accuracy; unused bandwidth; and unused memory.
 20. The system of claim 19, wherein the leader vehicle is dynamically selected because a designation of which member is the leader vehicle changes over time when a new connected vehicle with one or more of the following qualities becomes a member of the vehicular micro cloud: a faster processor than an existing leader vehicle; greater sensor accuracy than the existing leader vehicle; more unused bandwidth than the existing leader vehicle; and more unissued memory than the existing leader vehicle. 